DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-12 are currently pending and examined on the merits.
Priority
The instant application claims foreign priority to Application TW112106882 filed on 2/24/2023, in Taiwan. At this point in examination, the effective filing date of claims 1-12 is 2/24/2023.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 4/24/2023 and 2/1/2024 are in compliance with the provisions of 37 CFR 1.97. A signed copy of the corresponding 1449 form has been included with this Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-12 are directed to a cancer early detection method (process). Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1, 8, and 10 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claim 1 further recites:
establishing a miRNA expression profile database of cancer patient populations and healthy populations (i.e., mental processes);
establishing an analysis model for cancer early detection through following steps: a data quality control, a technical replicate merging, a calculation of normalization factors and data normalization, a biomarker feature selection, and a hyperparameter tuning (i.e., mental processes, mathematical concepts).
Dependent claim 8 further recites:
wherein the data quality control comprises checking a ratio (a missing value ratio) of any miRNA in the miRNA expression profile database which does not have an expression level (a missing value) in all training dataset samples (i.e., mental processes).
Dependent claim 10 further recites:
wherein the calculation of the normalization factor and the data normalization is to reduce a deviation between samples or batches by adjusting a data distribution or a normalization factor of a sample to be consistent (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Dependent claims 2, 4-7, 9, and 11-12 recite information further limiting the judicial exceptions indicated above.
Therefore, claims 1, 8, and 10 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claim 3 recites wherein the miRNA expression profile is determined by performing qPCR on a cDNA synthesized from a miRNA in the liquid biopsy sample. Performing qPCR on cDNA is considered a well-understood, routine, and conventional activity. Data gathering steps are extra-solution activity as they collect the data needed to carry out the JE. It does not impose any meaningful limitation on the JE or how the JE is performed (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.). Therefore, the claimed additional elements do not integrate the abstract ideas into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-12 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claim 3 contains additional elements that would not integrate a judicial exception into a practical application and is further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
The additional element of performing qPCR on a cDNA synthesized from a miRNA in the liquid biopsy sample (claim 3) is conventional. Evidence for conventionality is shown by Dave et al. (Laboratory Investigation, 2019, 99(4), 452-469). Dave et al. reviews reverse transcription qPCR (RT-qPCR) as a gold standard approach to quantify circulating miRNAs, and the principle of these RT-qPCR methods focuses on two steps: cDNA synthesis using reverse transcription followed by detection of amplified products using a conventional qPCR with either intercalating dye or TaqMan probe (pg. 455, col. 2, para. 2, lines 1-10). This shows performing qPCR on cDNA synthesized from an miRNA, which makes it a conventional practice in the art.
[Step 2B: NO]
Therefore, claims 1-12 are patent ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5 and 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 2020, 1866(10), 1-8), referred to as Zhang [A], in view of Kahraman et al. [US20190226032A1], Romani et al. (Theranostics, 2021, 11(6), 2987-2999), Marabita et al. (Briefings in Bioinformatics, 2015, 17(2), 204-212), Liu et al. (Life, 2021, 11(7), 1-39), and Lopez-Rincon et al. (Cancers, 2020, 12(7), 1-26).
With respect to claim 1:
Regarding the recited establishing a miRNA expression profile database of cancer patient populations and healthy populations, Zhang [A] discloses downloading expression profiles of 13 circulating miRNAs in 48 patients with lung cancer and 984 control samples (pg. 2, col. 1, para. 4, lines 1-3). This teaches an established miRNA expression profile database of cancer patient populations and healthy populations.
Regarding the recited establishing an analysis model for cancer early detection through following steps: a data quality control, a technical replicate merging, a calculation of normalization factors and data normalization, a biomarker feature selection, and a hyperparameter tuning, Zhang [A] discloses a two-step feature selection method to rank the discriminant features of circulating miRNAs for classifying lung cancer samples from control samples (pg. 1, Abstract, lines 6-8; pg. 2, col. 2, para. 3). This teaches a biomarker feature selection as part of a method for identifying significant circulating miRNAs that could be applied to early screening of lung cancer.
Regarding the recited the analysis model for cancer early detection includes normalization factors, a set of biomarkers, model weights and model hyperparameters, Zhang [A] discloses screening out top-ranked circulating miRNAs, which were determined to be potential early diagnosis biomarkers for monitoring lung cancer (pg. 4-6, col. 2, para. 3-7). This teaches a set of biomarkers that have potential for cancer early detection.
Regarding the recited wherein a miRNA expression profile in a liquid biopsy sample of a subject is analyzed by the analysis model for cancer early detection to be used as a basis for an early detection of cancer, Zhang [A] discloses expression profiles of 13 circulating miRNAs in 48 patients with lung cancer and 984 control samples, which were all detected from serum samples following liquid biopsy procedures, including blood collection, RNA isolation, and microRNA detection (pg. 2, col. 1, para. 4, lines 1-3). Also, further discloses a computational approach for identifying significant circulating miRNAs that may be applied to early screening, diagnosis, and monitoring of lung cancer progression (pg. 1, Abstract, lines 6-8). This teaches analyzing miRNA expression profiles from liquid biopsy serum samples of subjects using the computational method for early detection of cancer.
Zhang [A] does not disclose a data quality control.
However, Kahraman et al. discloses performing quality control and quantification of extracted RNA using Agilent’s Bioanalyzer (pg. 23, col. 1, para. [0309], lines 1-2). This teaches a data quality control.
Zhang [A] and Kahraman et al. do not disclose a technical replicate merging.
However, Romani et al. discloses using expressions of 40 miRNA replicates as technical replicates and quality control, and selecting only miRNAs with at least 75% of samples, either within patients or healthy controls, with more than 20% good quality replicates (pg. 2989, col. 2, para. 3, lines 1-11). The miRNA replicates within samples were summarized using mean values. This teaches merging technical replicates.
Zhang [A], Kahraman et al., and Romani et al. do not disclose a calculation of normalization factors and data normalization.
However, Marabita et al. discloses a normalization factor calculated as the geometric mean of selected normalizers for each sample, which is used to obtain the normalized relative quantities for each miRNA and sample (pg. 206, col. 1, para. 4). This teaches calculating normalization factors and using the normalization factors for data normalization.
Zhang [A], Kahraman et al., Romani et al., and Marabita et al. do not disclose a hyperparameter tuning.
However, Liu et al. discloses that the detection of cancer is regarded as a supervised problem in machine learning, and machine learning algorithms can help analyze and identify regularities from large cancer liquid biopsy datasets and predict future data as well (pg. 2, para. 3, lines 1-7). Also, further discloses hyperparameter tuning as part of the machine learning protocol (pg. 2, para. 3, lines 1-7; pg. 11, para. 5). This teaches hyperparameter tuning.
Zhang [A], Kahraman et al., Romani et al., and Liu et al. do not disclose normalization factors.
However, Marabita et al. discloses calculating normalization factors as the geometric mean of selected normalizers for each sample, which is used to obtain the normalized relative quantities for each miRNA and sample (pg. 206, col. 1, para. 4). This teaches normalization factors.
Zhang [A], Kahraman et al., Romani et al., Marabita et al., and Liu et al. do not disclose model weights.
However, Lopez-Rincon et al. discloses feature selection algorithms for selecting the most meaningful miRNAs to correctly classify cancer types, which define feature importance by the absolute value of the coefficients associated to each feature (pg. 3, para. 2, lines 1-3; pg. 3, para. 3-4). This teaches model weights.
Zhang [A], Kahraman et al., Romani et al., Marabita et al., and Lopez-Rincon et al. do not disclose model hyperparameters.
However, Liu et al. discloses the detection of cancer is regarded as a supervised problem in machine learning, and machine learning algorithms can help analyze and identify regularities from large cancer liquid biopsy datasets and predict future data as well (pg. 2, para. 3, lines 1-7). Also, further discloses hyperparameter tuning as part of the machine learning protocol and describes different methods of searching hyperparameter configurations (pg. 2, para. 3, lines 1-7; pg. 11-12, para. 5-9). This teaches model hyperparameters.
It would have been prima facie obvious to one of ordinary skill in the art to modify the cancer early detection model disclosed by Zhang [A] to incorporate data quality control disclosed by Kahraman et al., technical replicate merging disclosed by Romani et al., normalization factors disclosed by Marabita et al., model weights disclosed by Lopez-Rincon et al., and model hyperparameters disclosed by Liu et al. One would be motivated to incorporate data quality control, technical replicate merging, normalization factors, model weights, and model hyperparameters in the cancer early detection method because the approach disclosed by Kahraman et al. implements a multiple biomarker strategy that implements sets of miRNA biomarkers for the diagnosis of breast cancer, which could further increase specificity, sensitivity, accuracy, and predictive power (pg. 1, col. 2, para [0004]). Incorporating data quality control from this approach into the cancer early detection method could increase accuracy and predictive power. Romani et al. discloses that their miRNA analyzation method was able to identify reliable reference miRNAs used to normalize miR-106b-5p, miR-423-5p, and miR-193b-3p expression levels, obtaining the most accurate measure of expression (pg. 2998, col. 1, para. 1, lines 21-27). Therefore, incorporating technical replicate merging from this method will increase accuracy in cancer early detection. Marabita et al. discloses that the use of an accurate normalization procedure will ensure better reproducibility (pg. 209, col. 2, para. 1, lines 8-10). This means incorporating normalization factors will make the cancer early detection method more reliable and results will be more trustworthy. Lopez-Rincon et al. discloses that applying a recursive ensemble feature selection algorithm ensures high-quality classification (>90% mean classification accuracy over all the ensemble) (pg. 16, para. 9, lines 5-8). Incorporating model weights from this approach will ensure high quality early detection of cancer. Liu et al. discloses that simple machine learning algorithms such as linear models can lead to a high-quality performance for liquid biopsy-based diagnosis for several common cancer types (pg. 31, para. 5, lines 1-3). Therefore, incorporating model hyperparameters, which are part of the standard machine learning process, will lead to high quality performance in early cancer detection. There is a likelihood of success, since all methods taught are used for analyzing miRNA expression to identify relevant biomarkers in relation to cancer, which are well known in the field of oncology.
With respect to claim 2:
Zhang [A], Romani et al., Marabita et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein the miRNA expression profile is determined by qPCR, sequencing, microarray, or RNA-DNA hybrid capture technology.
However, Kahraman et al. discloses determining miRNA levels representative of breast cancer in a blood sample from a patient by nucleic acid amplification, which may be performed using real time polymerase chain reaction (RT-PCR) such as real time quantitative PCR (RT qPCR) (pg. 10, col. 2, para. [0093], lines 1-15). This teaches that miRNA expression can be determined by qPCR.
With respect to claim 3:
Zhang [A], Romani et al., Marabita et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein the miRNA expression profile is determined by performing qPCR on a cDNA synthesized from a miRNA in the liquid biopsy sample.
However, Kahraman et al. discloses steps for the real time polymerase chain reaction (RT-PCR) including extracting total RNA from the blood sample isolated from the patient and obtaining cDNA samples by RNA reverse transcription reaction using miRNA-specific primers (pg. 10, col. 2, para. [0093], lines 1-15; pg. 10, col. 2, para. [0093], lines 20-24). This teaches determining miRNA expression by performing qPCR on cDNA from miRNA in liquid biopsy samples.
With respect to claim 4:
Zhang [A], Romani et al., Marabita et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein the miRNA expression profile comprises an expression level of a plurality of miRNAs.
However, Kahraman et al. discloses determining the level of at least one miRNA representative for breast cancer in a blood sample from the patient (pg. 1, col. 2, para. [0005], lines 4-5). This teaches an expression level of a plurality of miRNAs.
With respect to claim 5:
Kahraman et al., Romani et al., Marabita et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein a type of the early detection of cancer comprises lung cancer.
However, Zhang [A] discloses a computational approach for identifying significant circulating miRNAs that may be applied to early screening, diagnosis, and monitoring of lung cancer progression (pg. 1, Abstract, lines 6-8). This teaches a type of early detection of cancer comprising lung cancer.
With respect to claim 7:
Kahraman et al., Romani et al., Marabita et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein the analysis model for cancer early detection is established based on a classification algorithm, and the classification algorithm includes Logistic Regression or Random Forest.
However, Zhang [A] discloses incremental feature selection (IFS) with random forest (RF) as the classification algorithm used to extract key circulating miRNAs (pg. 2, col. 1, para. 3, lines 11-14). Also, further discloses that this feature selection procedure is part of the computational approach for identifying significant circulating miRNAs that may be applied to early screening, diagnosis, and monitoring of lung cancer progression (pg. 1, Abstract, lines 6-8). This teaches that an analysis model for cancer early detection is based on a classification algorithm, which includes random forest.
With respect to claim 8:
Zhang [A], Kahraman et al., Romani et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein the data quality control comprises checking a ratio (a missing value ratio) of any miRNA in the miRNA expression profile database which does not have an expression level (a missing value) in all training dataset samples, and the miRNA of which the missing value ratio is 0 can be used as a normalization factor.
However, Marabita et al. discloses that high-throughput data may contain missing values and a solution would be to only consider miRNAs with complete observations. The rationale for this hard filtering would be that circulating miRNAs serving as endogenous controls should be abundantly and stably detected in all samples to be considered for analysis, and therefore, missed detection of a specific miRNA in a subgroup of samples would undermine its ‘candidature’ as normalizer (pg. 206, col. 2, para. 5, lines 1-9). This teaches filtering out miRNAs that have missing expression levels and only using miRNAs with expression levels as a normalizer, which can be used to calculate the normalization factor.
With respect to claim 9:
Zhang [A], Kahraman et al., Marabita et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein when there are technical replicate data of a same sample in a training dataset or a testing dataset, the technical replicate merging is performed.
However, Romani et al. discloses using expressions of 40 miRNA replicates as technical replicates and quality control, and selecting only miRNAs with at least 75% of samples, either within patients or healthy controls, with more than 20% good quality replicates (pg. 2989, col. 2, para. 3, lines 1-11). The miRNA replicates within samples were summarized using mean values. This teaches merging technical replicates of same samples.
With respect to claim 10:
Zhang [A], Kahraman et al., Romani et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein the calculation of the normalization factor and the data normalization is to reduce a deviation between samples or batches by adjusting a data distribution or a normalization factor of a sample to be consistent.
However, Marabita et al. discloses a normalization factor calculated as the geometric mean of selected normalizers for each sample, which is used to obtain the normalized relative quantities for each miRNA and sample (pg. 206, col. 1, para. 4). Also, further discloses that the effect of this normalization process is highlighting true biological changes and eliminating or reducing the variability introduced during the whole process (pg. 206, col. 1-2, para. 6, lines 1-3). Marabita et al. discloses different algorithms for identifying stable miRNAs, which generate scores that will determine the top candidate miRNAs for calculating the normalization factor (pg. 207, col. 1-2, para. 1-6). This teaches calculating normalization factors and using the normalization factors for data normalization. The process of normalization reduces variability and the normalization factor is adjusted based on the scoring and ranking of the miRNAs.
With respect to claim 11:
Zhang [A], Kahraman et al., Romani et al., Marabita et al., and Liu et al., do not disclose wherein the biomarker feature selection is to remove noises caused by non-correlated features according to a weight or an importance of features.
However, Lopez-Rincon et al. discloses recursive feature selection, which scores each feature, or expression values of miRNAs, depending on its usefulness for classifying cancer types. Features with the lowest scores are then removed, and the process is iterated with the remaining features until the overall classification accuracy drops below a threshold or when a user-defined number of features is reached (pg. 3, para. 2, lines 1-7). This teaches biomarker feature selection that removes less useful features that may be non-correlated for classifying cancer types.
With respect to claim 12:
Zhang [A], Kahraman et al., Romani et al., Marabita et al., and Lopez-Rincon et al., do not disclose wherein the hyperparameter tuning is used to find out a hyperparameter setting suitable for a dataset.
However, Liu et al. discloses that hyperparameters of machine learning algorithms enable the model to be tailored to different datasets, and describes different hyperparameter tuning methods that search for an appropriate hyperparameter configuration (pg. 11-12, para. 5-9). This teaches hyperparameter tuning used to find hyperparameter settings tailored to different datasets.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 2020, 1866(10), 1-8), referred to as Zhang [A], Kahraman et al. [US20190226032A1], Romani et al. (Theranostics, 2021, 11(6), 2987-2999), Marabita et al. (Briefings in Bioinformatics, 2015, 17(2), 204-212), Liu et al. (Life, 2021, 11(7), 1-39), and Lopez-Rincon et al. (Cancers, 2020, 12(7), 1-26) as applied to claims 1-5 and 7-12 above, in view of Zhang et al. (European Urology Focus, 2018, 4(3), 412-419), referred to as Zhang [B].
Zhang [A], Kahraman et al., Romani et al., Marabita et al., Liu et al., and Lopez-Rincon et al. are applied to claims 1-5 and 7-12 above.
With respect to claim 6:
Zhang [A], Kahraman et al., Romani et al., Marabita et al., Liu et al., and Lopez-Rincon et al. do not disclose wherein the liquid biopsy sample comprises plasma, serum, or urine, and exosomes further purified from the liquid biopsy sample.
However, Zhang [B] discloses extracting and purifying exosomes from serum samples to selectively capture exosomes positive for tumor-associated epithelial cell adhesion molecule (EpCAM) using a magnetic bead technique. Total RNA was extracted and expression levels of miRNAs were quantified (pg. 412, Abstract, lines 6-11 and lines 28-30). This teaches a liquid biopsy sample comprising serum and exosomes are purified from the sample.
It would have been prima facie obvious to one of ordinary skill in the art to modify the cancer early detection model disclosed by Zhang [A], Kahraman et al., Romani et al., Marabita et al., Liu et al., and Lopez-Rincon et al. to incorporate purifying exosomes from a liquid biopsy serum sample disclosed by Zhang [B]. One would be motivated to incorporate the purification process because Zhang [B] discloses that all miRNAs obtained had good quality for amplification (pg. 418, col. 1, para. 2, lines 1-4). This means incorporating the purification process will improve quality of miRNAs used in the cancer early detection method. There is a likelihood of success, since all methods taught are used for analyzing miRNA expression to identify relevant biomarkers in relation to cancer, which are well known in the field of oncology.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jammy Luo whose telephone number is (571)272-2358. The examiner can normally be reached Monday - Friday, 9:00 AM - 5:00 PM EST.
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/J.N.L./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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